Supercharge Your Chatbot with ChatGPT's AI-Generated conversations
Welcome to an exciting new world of chatbot creation! Today, we're thrilled to share with you an innovative feature that will revolutionize the way you build ...
If you haven't heard of open AI, you must have been living under a rock 🤣
OpenAI is taking over the world and changing the game for digital marketers! 🤖🔥
It's all working great! People are thrilled about this revolutionary AI tool!
But there is huge problem when you want to use OpenAI for your own business! 🖼️
Imaging, you build a OpenAI powered chatbot, and the user is asking custom support details.....
And your chatbot is popup up fake emails & make up phone numbers.
(The reasons is openAI is learning from tons of data ,but it might not have up to date data about your business or not having your business data at all. Remember AI is only as good as the information it has access to.)
So in order to solve this problem, you need to "feed" or "train" Open AI to understand & learn your business, so that you can really "harness" the power of OpenAI to server your business.
We will discuss in-depth how you can build the chatbot powered by OpenAi that fully understand your business, generate accurate & relevant response to your customers.
At the end of the article, you can also build your own OpenAI chatbot tailed to your own business in 5 minutes, 3 simple steps.
Are you ready to start this wonderful journey with us?
Let's start with some foundation about OpenAI 🙃
(Skip it if you already familiar with it)
What's openAI?
So everyone is talking about it... but
What is it? 🧐
Let me explain it in a way that you can REALLY understand. 👇
In 2015, OpenAI was created by a group of tech-savvy individuals including Elon Musk, Sam Altman, and others, with a simple yet bold mission: to build safe and open AI tools that would enhance people's lives, instead of being a threat to it.
(Credit:taskade.com)
Since then, they've created some amazing tools like DALL·E for AI image generation and CodexAI for powering GitHub's coding tool CoPilot.
But, their biggest hit came last year with ChatGPT - a mind-blowing AI tool based on the company's flagship GPT-3 model.
It's more than just a supercharged autocomplete, and it's made OpenAI the talk of the tech world.
Let's take a step back and learn about the history of this impressive company.
Start from 2015
When former Y Combinator CEO Sam Altman and entrepreneur Elon Musk initiated an effort to promote the safe and open development of artificial intelligence.
(Sam Altman and Elon Musk during a conversation at a Tesla plant.Image credit: Y Combinator
Altman and Musk, even prior to starting the company, had voiced their apprehensions about the dangers and possibilities of AI technology, referring to it at times as "the greatest danger to humanity."
Shift and Expansion (2017-2019)
During the subsequent two years, OpenAI redirected its focus to broader AI research and development.
In 2018, the company published a paper titled "Improving Language Understanding through Generative Pre-Training," which introduced the idea of a Generative Pre-trained Transformer (GPT).
GPTs are neural networks, machine learning models modeled after the structure and functioning of the human brain, trained on a vast dataset of text generated by humans.
They are capable of performing various tasks such as generating and answering questions, among others.
The OpenAI team put their ideas into action and developed GPT-1, their first language model, which was "trained" on the BookCorpus consisting of over 7,000 unpublished books.
The model eventually advanced into GPT-2, a more robust version, which was trained on 8 million web pages and comprised 1.5 billion parameters (trained values that enable text prediction).
They are capable of performing various tasks such as generating and answering questions, among others.
(GPT-2 playground with model & decoder settings on the left)
DALL·E, GPT-3, and ChatGPT (2021 and beyond)
In 2021, OpenAI unveiled DALL·E, an AI utilizing a similar architecture to GPT-2. Unlike its predecessor, DALL·E - a combination of the names WALL-E and the Spanish surrealist artist Salvador Dalí - was capable of producing photorealistic images seemingly from scratch.
(A DALL-E 2 artwork pinboard. Source: Pinterest)
The following year, OpenAI once again raised the bar with the release of GPT-3. This iteration of the previous models was fed 45TB of text data and had 175B parameters. It was smarter, quicker, and more formidable than anything seen before.
To make this possible, Microsoft created a supercomputer for OpenAI that included 285,000 CPU cores and 10,000 GPUs. It ranked #5 on the Top500 list of supercomputers.
(GPT-2 playground with model & decoder settings on the left)
The success of GPT-3 led to the creation of another remarkable tool. In November 2022, OpenAI introduced ChatGPT, a language model chatbot built on the GPT-3 platform.
One of the most astonishing features of ChatGPT is its ability to comprehend context - the chatbot can generate answers and adjust them based on the conversational history.
This enables you to "train" ChatGPT within a conversational thread for more accurate responses.
Many people's first encounter with AI, through ChatGPT or DALL-E 2 (which was released in the same month as DALL-E), was a conscious and surreal experience. While it may not have been a case of love at first sight, it is evident that AI is the future and it has already arrived.
Usercase of OpenAI
OpenAI is making a splash in the digital marketing world! 💦
Businesses are loving how it can streamline their processes and enhance their customer interactions.
Here are a few user cases 👇
🤖 A customer service chatbot that uses openAI to provide quick and accurate answers to common customer queries, 24/7.
🛍️ An e-commerce website that utilizes openAI to provide personalized product recommendations and answer shopping-related questions in real-time.
🚗 A car dealership that uses openAI to help car shoppers find the perfect vehicle by answering questions about features, pricing, and financing options.
💬 A HR chatbot that uses openAI to assist employees with HR-related questions, such as benefits, time off, and company policies.
💻 A virtual assistant for remote workers that uses openAI to help with scheduling, email management, and task tracking. 💻
With its cutting-edge language model, businesses are loving how it can streamline their processes and enhance their customer interactions.
💬 From crafting the perfect response to a customer query, to generating creative ideas in a flash, OpenAI is quickly becoming a go-to tool for digital marketers everywhere! 💡
Well, there are more cool user cases coming up everyday.
We will keep this list updated.
Difference between OpenAI & ChatGPT
Most of the people don't under the difference. 😋
Lucky for you, after this section, you won't be one of them 👍
Well, Let me make it super simple for you to understand:
OpenAI: The Brain 🧠 behind the magic of AI language models.
ChatGPT: One of the models 🤖 developed by OpenAI, specialized in chat-based interactions.
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OpenAI is like the chef 🍳, while ChatGPT is one of the dishes 🍔 it can cook up.
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OpenAI has the capability to do more than just chat, like language translation 💬, text summarization 📄, and more!
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ChatGPT, on the other hand, is specifically trained for chat-based interactions 🗣️ and can generate human-like responses.
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OpenAI has the power 💥 to create multiple models, each tailored to specific tasks. ChatGPT is just one of them.
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So, if you're looking for a powerful AI language model for your chatbot, ChatGPT is a great choice! 🤩
But ChatGPT don't have the open API yet, only OpenAI offer the API for now for the integration with your chatbot.
So most of the chatbot platform you have seen is using OpenAI to power the chatbot bundling.
UChat have OpenAI integration natively of both built-in already in their platform.
How to train openAI to understand your business?
So we covered the history of OpenAI and also the difference between openAI & ChatGPT.
And, you can build AWESOME user cases with openAI, especially the chatbot.
But there is one very BIG problem!
Even though OpenAI trained based on 45TB of text data that translated into 175B parameters. But it's a "Black Box".
You never know if they have training on your business data or not.
Plus, even they do trained on your business data, there is no control of what the data they trained on.
This can be scary for most of the business. 😱
If you let OpenAI get involved in responding to your customers, it's like let your customer ask "Internet" for answers. and the un-monitored response might put your business in jeopardy.
There is a solution for this BIG problem! 😄
You can "Feed the data" into OpenAI!
And then, OpenAI will know Exactly what your business is, what you are offering, what is the most asked questions & replies....
Then, you can actually have the chance to "harness" the power of OpenAI, and actually implement it into your business.
Sound like something you want to find out?
See you in the next section 👇
Compare of fine-tuning & openAI embedding
OpenAI have invested a lot of time and resources to build some of the most advanced AI technology out there, including a tool that can detect a user's intent with amazing accuracy! 🔍 🤖
This means that when you use OpenAI's language models, your chatbot will be able to understand and respond to user requests in a more natural and effective way. 💬
OpenAI actually offered 2 different way for you to "feed the data" or "train" OpenAI with your own business related data.
Build your own fine-tuning model or Set up OpenAI embedding!
You can "feed the data" to train OpenAI in both ways.
After you complete the training, then the OpenAI "bot" now will be using your own database to reply to your customers.
They are not asking "internet" for the response anymore.
Finally you can harness the power of OpenAI to server your own business!
Here is some help documentation on fine-tuning & Embeddings.
Let me explain the difference from purpose, time required, adaptability, accuracy, resource requirement to help you understand the difference between these 2 different methods.
Here is a few benefits of using OpenAI embedding in your chatbot:
1. Quick and Accurate Results 🚀
OpenAI embeddings are pre-trained on vast amounts of data, allowing them to provide quick and accurate results.
2. Requires Less Training Data 📈
Embeddings have already been trained on a large dataset, so you don't need as much data for fine-tuning. This makes it easier to get started with openAI embeddings, especially for smaller businesses.
3. Easier to Use 💻
Embeddings are pre-trained, so you don't need to spend as much time and resources on training the model. This makes the process of setting up and using openAI embeddings much easier.
4. Enhanced Customization 🎨
Embeddings can be combined with other models to create a more customized solution for your business needs.
5. Better Results 🏆
Embeddings provide better results compared to fine-tuning because they have already been trained on a large dataset, allowing them to make more accurate predictions.
6. Cost-Effective 💰
OpenAI embeddings are a cost-effective solution compared to fine-tuning, as they require less training data and time to set up.
7. Improved User Experience 🤩
OpenAI embeddings can provide a better user experience as they are pre-trained and can provide quick and accurate results.
In conclusion, OpenAI Embedding is a better solution for businesses as it's faster to implement, highly adaptable, and requires fewer resources compared to Fine-Tuning.
It offers businesses the opportunity to leverage the power of OpenAI's pre-trained language model without the need for extensive training and fine-tuning. 🚀
UChat is the first chatbot platform that have bring OpenAI embedding feature in their platform.
In the next section, we will talk about the 3 biggest advantage of implement OpenAI embedding in your chatbot building. 👇
3 Biggest advantages of using openAI embedding
#1: Generate accurate response from your own database
So you have known that both fine-tuning model & embeddings can be trained on your own data....
Besides all the differences mentioned in the last section, there is another one big difference been left out on purpose.....
So here it is..
If you are using fine-tuning model, after the model is training, you can use this model to generate response...
Check the below screenshot from UChat's built-in OpenAI integration 👇
There is an action in UChat called "Create Text Completion", you can use the default "text-davinci-003" engine to generate response or use your own fine-tuning model to generate a completion.
But there is no control to check the qualify of the response(even it's already high quality & relevant response), it's a quick & straight away action.
But if you are using embedding, you can have the opportunity to check a "quality score" or "confidence score" to measure the quality of the response.
Please check the screenshot from UChat built-in OpenAI embedding action below 👇
UChat have built in OpenAI actions in the platform, and you can see from the screenshot above: Embedding Match is one of the actions, you can search the user's response from OpenAI embedding database.
So, depending on the "quality score" you can either:
Generate a completion straight away
Save the context, and then use another action "embedding match and completion" to use that context as the prompt.
Display relevant answer if the quality score is too low
The possibility is endless.
If you are using OpenAI embedding, you will have the maximum flexibility to redirect the user to other directions of automation.
If this is a little too hard for you to understand, don't worry about it.
#2: Show highly relevant answer from your database
So, in the real case scenerios, you may not have any relevant data from your database.
So it's impossible for your OpenAI assistant to generate any proper response.
You definitely don't want OpenAI to make things up out of the thin air, even sometimes they get pretty decent answers, thanks to their advanced language model.
The possible solution is:
- If the confidence score is lower than certain value, display the most relevant answer to the user
- Offer the human live chat support or alternative ways to offer support.
You can easily do that through simple condition check and display the relevant answers in cards in UChat.
Check the screenshot below 👇
Generally speaking, if the score is more than 0.8, it's consider highly relevant & quality match with answers from your own database.
You can save the matched context and used as prompt in the next "Embedding Match & Completion" action to generate a high quality and high relevant reply.
If the confidence score is lower than 0.8, you have the option to offer the most relevant data & display in card, and also offer alternative to reach out for support.
Inside of UChat, our platform have very easy to use JSON variable and for each action. so you can easily saved all the matched high relevant answers from OpenAI, and then display the results in cards.
There you have it.
With the OpenAI embedding, you have the best option of the two worlds 🌍
You can generate highly quality & relevant answers directly, and also have the ability to monitor and redirect the user to different route if there is no highly relevant replies.
Now, let's get to another HUGE advantages. 👇
#3: Flexible to ask follow up questions
Imaging you are building a OpenAI powered chatbot for your restaurant 🧑🍳
Everything is great. the chatbot is handling a lot of FAQ for you. ✌️
(there should be a happy dance here 💃)
Now, there is a customer asking :"How can I make a reservation?"
The chatbot is generating a accurate response from your database, provided the phone number to call & website URL to fill up the forms.....
And it's stopped there..... ⌛
If the customer got interrupted, and the conversation would just died ❌
The chatbot is doing his job well, but he can do his job better if we set up the follow up question based on the user's intent 🤖
By using OpenAI embedding, you can steer the conversation towards your end goal by asking follow-up questions or redirecting it to your desired call-to-action. 🎯
In the previous examples, if the chatbot detect the user's intent is booking reservation, after the response generated, the chatbot can immediately ask the follow up question:
"Do you want to book your reservation now?"
One simple step, but it's big improvement for business.
You can easily not only reply to the user's question, but offer the options depending on various user's intent.
Let me show you a quick screenshot how you can do this inside of UChat platform:
So you can trigger the follow up question in 2 simple steps in UChat.
Step 1: Save the matched user's intent into user's custom field(you can choose to save only the confidence score is more than 0.8)
Step 2: After the OpenAI completion, and send the response to the users, follow up a condition check, if the user's intent matched with your target, redirect to another flow to follow up.
That's the 3 biggest advantages if you choose to use OpenAI embedding to build chatbot for your business.
Step by step to build your first openAI powered chatbot
I hope you are excited as I am 🙃
You will learn some quick steps to set up your first OpenAI powered chatbot with UChat. 🤖
If you want to use the latest chatbot technologies to help your business grow, and reduce customer support, you don't want to miss this.
Ready to get started?
#1: Checklist
You will need to get below items ready to have a quick start:
- OpenAI account with API key ready
- UChat account(you can sign up 14 days free trial account, no credit card required)
- Some business embedding data to import and trained OpenAI.
If you join our free mini workshop from the link here, you will easily set up everything within 5 minutes, we even provide the ready to use template installed right into your free trial account.
It will save you tons of time.
#2: Generate OpenAI embeddings
This is the step that you provide a database that is related to your business.
After you upload your data into OpenAI from the integration in UChat, don't forget to press the "regenerate" button.
Check the short video below how you can set up your own embedding or import csv file to "train" your OpenAI 👇👇
#3: Familiar with UChat OpenAI integration
After you finished generate OpenAI embedding, you can go to any flow, and right click to trigger to add new nodes.
Select Action -> Integrations -> OpenAI
And you will see the below options UChat Support:
Create Text Completion: You can use the default davinc3 or your fine-tuning model to generate response
Image Generation: You can even dynamically generate images
Embedding Match: This action will search your own database for the match.
Embedding Match and Completion: This action can use the highest match from Embedding Match as context in the prompt to generate relevant response.
Check below video for more information about UChat's native integration with OpenAI.
#4: Generate response from OpenAI embedding search
Now you are familiar with UChat's built-in OpenAI integration.
It's time to actually use the OpenAI embedding to generate response to your users.
Make sure you understand the difference between these 3 actions:
The "Create Text Completion" action is used if you want to use your own fine-tuning model or default davinc003 model to generate response.
If you are using the OpenAI embedding as showed in this blog post, you should use "Embedding match" or "Embedding match & completion" to generate response or show relevant response if the "confidence score" isn't high enough.
Deployment chatbot across 15+ channels
f you have reading up until now, you already "harness" the power of OpenAI.
UChat's native OpenAI integration can also help your business:
- Reply Facebook Page post comments
- Reply Instagram post comments
- Reply Instagram DM messages
- Reply Instagram Story mentions & Stories replies
.
.
Even write up the Facebook post and post directly from UChat platform.....
All the above mentioned automation is based on your business data training, all the replies will be highly relevant & high quality.
Now, you have this powerful, smart, 7x24 assistant is helping you around the clock ⏰🕰️
🌎 But now, your customers are coming from all over and using different social channels.
💬 Omni-channel is important in today's world because it allows businesses to communicate with their customers through multiple channels, such as web chat, messaging apps, voice assistants, and more.
Here is a list of channels that supported right in UChat platform:
✅ Web Chat
✅ Messenger
✅ Instagram
✅ WhatsApp
✅ Google Business Messenger
✅ SMS
✅ Voice
✅ Telegram
✅ Line
✅ Viber
✅ WeChat
✅ VK
✅ Slack
✅ Jivo Chat
✅ Intercom
..
..
💬 This helps businesses reach their customers wherever they are.
You can sign up UChat for a 14 days free trial, no credit card required, but access to all the pro features.
Conclusion
What we've learned so far
🦾🤖🦿
We started with background information about OpenAI, and then learn a little bit about the history and development of this technology.
Even it's very cool, and have HUGE potential.
But there will a big bottleneck when you want to implement this "magic brain" 🧠 to learn, know, master your business
Without that, OpenAI will be powerless for your particular business.
With the fine-tuning model and OpenAI embedding, you do have the options to make the OpenAI works for your business.
But there is pros & cons for these different solutions.
We would suggest you go with OpenAI embeddings, it's so easy to set up, and also easy to scale and manage.
If you want to find more about the OpenAI integration with UChat, you can connect with our social networks below 👇
- Join our Facebook Group here
- Subscribe to UChat Youtube Channel
If you need one of our team member to book a demo with UChat team, here is the link to do so.
- Book a demo with UChat
If you need any help or have any question, you can send to our support email at ticket@uchat.com.au
P.S: You can have 14 days free trial with UChat, no credit card required, access to all the pro features.
